Categories: DQDeepTech

Overcoming the challenges in AI deployment for financial sector

Artificial intelligence and machine learning are gaining traction in the financial services industry, as companies see the value in automating important procedures and making greater use of current data. According to Business Insider, 56 percent of banks use AI for risk management, and 52 percent use it to generate money from new goods and services.

However, there are still difficulties. While 74% of banking executives believe new technologies would alter the business, they are concerned about impediments to effective implementation, such as expanding skills gaps and increased complexity.

Making the transition to AI and ML for financial businesses necessitates an awareness of major benefits, exploration of common difficulties, and adoption of best practices.

AI’s Emerging Advantages for Banks

Despite the fact that machine learning tools have long been utilised for credit scoring, fraud detection, anti-money laundering, and know-your-customer applications, AI-driven natural language processing technologies now allow banks to integrate AI into a range of different business processes. Intelligent work automation, document processing, predictive analysis, and personalization of customer care are just a few examples.

Simply put, banks can swiftly examine enormous data sets because to the self-learning nature of new AI and ML systems. Given both the expanding volume of data generated by banks and the variety of structured and unstructured data available, human procedures simply can’t keep up.

AI solutions can also assist digital-first fintech companies reduce client turnover by allowing them to provide on-demand account access, loan approvals, and investment advice.

Overcoming Obstacles to AI Adoption in Banking

  1. Siloed data:

To break down silos, machine learning operations (MLOps) and data operations, comparable to DevOps in application development, are now being used. However, banks’ massive amounts of segregated data make uniformity and usefulness difficult.

  1. Multiple Stakeholders:

Data scientists, data engineers, software engineers, and deployment engineers are among the numerous people involved in AI initiatives, each with their own preferences for technology tools and how they work. Banks frequently struggle to establish a unified approach that works for everyone, given the huge array of tools, frameworks, and AI technologies available.

  1. Managing current infrastructure:

Both deployment and MLOps engineers are plagued by inflexible infrastructure, a lack of uniformity, and changing technologies, forcing ongoing repackaging and integration across bank IT systems.

  1. Myriad processes:

Given the many processes involved in AI, such as data ingestion, analysis, transformation, and validation, model development, validation, and monitoring, as well as logging and training, IT is under a lot of pressure to implement a forward-thinking data centre infrastructure or hybrid cloud strategy to support scalability for data science users and processes.

This complication can cause delays in AI adoption at scale, lengthening the time between deployment and a solid return on investment. As a result, executive sponsors and organisational stakeholders may lose focus and faith.

Best Practices for Getting the Most Out of AI in Financial Sector

Financial institutions require component-based best practises that address a number of business deployment concerns, such as:

  • Data Architecture– Finding and maintaining verified and curated data is crucial for AI projects because high-quality data makes it easier to train machine learning algorithms, which reduces time to market.
  • Hybrid cloud- As banks use both on-premises and cloud services, a hybrid cloud approach is critical. Container and Kubernetes techniques are useful for managing AI and machine learning in a variety of IT contexts.
  • Infrastructure– To support scalability, performance, streamlined provisioning, and data security, infrastructure adoption must be forward-looking.
  • MLOps– To boost deployment speed and solution usability, banks could use enterprise solutions that aim to operationalize ML and AI by breaking down barriers, merging workflows, delivering data integration, and utilising open-source tools and frameworks.

By exploiting data resources at scale, banks can minimise risk, improve customer service, and automate important processes using artificial intelligence and machine learning. Banks, on the other hand, must solve critical obstacles and adopt best practices across organisational IT infrastructure to efficiently migrate from traditional to cutting-edge.

 

Authored by Tanisha Gupta.

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